As demand on high performance computation continuously increases, the traditional Von Neumann computer architecture becomes less efficient. In recent years, neuromorphic hardware systems have gained great attentions. Such systems can potentially provide the capabilities of biological perception and information processing within a compact and energy-efficient platform. Systems such as these can be used in different computing platforms for high performance computing under size, weight and power (SWaP) constraints. Potential Air Force applications, for example, include autonomy and autonomous systems, communication and networking, intelligence data analytics and cyber situational awareness.
To this end, a resistive crossbar array based computing system has the potential to enable high-efficient, large-scale signal processing. Such systems, for example, can dramatically improve the capabilities in pattern recognition used in modern artificial intelligence systems. In such systems, a crossbar array of resistive memory devices is used to carry out signal processing computations.